Composite Learning Control of Flexible-Link Manipulator Using NN and DOB
研究了基于奇异摄动理论的柔性连杆机械臂复合学习控制，利用神经网络逼近系统不确定性、扰动观测器估计复合扰动，结合预测误差提出复合学习算法，仿真表明能显著提高跟踪精度。
This paper investigates the singular perturbation (SP) theory-based composite learning control of a flexible-link manipulator using neural networks (NNs) and disturbance observer (DOB). For the dynamics, the system states are separated into fast and slow variables in terms of time scale. For the multi-input-multi-output slow dynamics, the intelligent control is designed where NNs are used for system uncertainty approximation and the DOB is used for compound disturbance estimation. The main contribution is that a novel controller using NN and DOB is constructed to deal with unknown dynamics and time-varying disturbances while the composite learning algorithm is proposed with prediction error. For the fast dynamics, sliding mode control is employed. The boundedness of the tracking error is proved via Lyapunov approach. The simulation results show that the DOB-based composite neural control can greatly improve the tracking precision.